A multitude of wireless communications systems are in common use today. Mobile telephones, pagers and wirelessly-connected computing devices such as personal digital assistants (PDAs) and laptop computers provide portable communications at virtually any locality. Wireless local area networks (WLANs) and wireless personal area networks (WPANs) according to the Institute of Electrical and Electronic Engineers (IEEE) specifications 802.11 (WLAN) (including 802.11a, 802.11b, 802.11g, 802.11n, etc.), 802.15.1 (WPAN) and 802.15.4 (WPAN-LR) also provide wireless interconnection of computing devices and personal communications devices, as do other devices such as home automation devices.
Within the above-listed networks and wireless networks in general, in many personal, commercial and industrial applications it may be desirable to map and monitor the wireless coverage of network devices in an area.
In addition, in many of those networks also including location capabilities it may be desirable to monitor mobile wireless devices and RFID tags and estimate their location.
Having a reliable representation of the actual wireless signals propagation behavior in a communication system may be valuable for enabling optimal performance. This may be needed for site planning, cell partition and channel assignment, mobile device roaming, transmission power tuning, etc. In many systems, a wireless coverage mapping is performed during the initial design process and thereafter, systems do not dynamically react to environmental changes, equipment failures or changes, deployment changes, or any other change that might influence wireless coverage.
In other wireless communication systems, the received signal strength of signals transmitted and received by network devices (e.g. WLAN Access Points) in the same area, is measured. While this technique may provide a more dynamic picture of the wireless signals propagation behavior, it may still be limited to the quality of reception related to the network devices themselves. This technique may not provide sufficiently accurate information regarding the wireless signals propagation behavior of wireless mobile units in that area.
Some wireless communication systems include the use of Wi-Fi RFID tags, which have significantly increased in recent years. Wireless tags may be used, for example, for tracking assets, in many industries such as, for example, healthcare, manufacturing, logistics, retail, oil and gas.
Although there are many methods may be used to locate mobile wireless devices, using Received Signal Strength Indicator (RSSI) measurements became very popular for the following reasons:                Performing measurements may be performed with relative technical simplicity. Many commercial IEEE802.11x Access Points or other radios have the ability to measure the RSSI of received signals.        Relatively good location accuracy may be achieved in a variety of indoor and outdoor environments. This accuracy can be around few meters (1-3 meters).        Location system simplicity. RSSI-based location systems may be simpler to deploy (e.g. many do not require special synchronization techniques or special hardware).        Most wireless mobile devices can be located.        
In many wireless networks, signal strength measurements along with other information are used to estimate the expected wireless coverage (typically referred as radio map) of the network devices (e.g. Access Points, base stations, etc.) in an area. In wireless location systems, that radio map may also be used to estimate the location of wireless mobile devices.
In other cases, the mobile wireless device itself measures the received signal strength of signals transmitted by network devices and transfers those measurements to a network server. Those signal strength measurements may be used both to generate the radio map and to estimate the location of that mobile device. Moreover, once a radio map of an area was generated and transferred to a mobile device operating in that area, this device may have the ability to calculate its own location without involving a network server in this process.
There are many algorithms based on received signal strength (RSSI) which are used to estimate the location of a wireless device. Pioneering research proposed a technique called RF fingerprinting that uses empirically measured signal strengths to estimate a wireless mobile unit location. The location software calculates the location of the mobile device using the received signal strength measurements, sometimes together with additional RF signals propagation characteristics such as reflection indexes, attenuation levels, signal strength variance of wireless signals related to wireless devices at known points, data which is further used to map the wireless signal propagation behavior in an area.
The RF fingerprinting data is arranged in what is typically denominated as “radio maps”. Generally speaking, radio maps provide the wireless signals propagation characteristics related to zones in an area. Typically those zones are square zones represented by a single point in a grid but in more generic embodiments, zones may be represented by any type of polygon.
In one implementation of the above described radio maps, the wireless signals propagation characteristics comprise the expected signal strength of received signals at certain zones (represented by points) in an area, either when the wireless signals are transmitted by mobile devices (at the corresponding points) and received by network devices or vice versa (the signals are transmitted by the network devices and received by the mobile devices at those zones in the area).
In other implementations of the radio maps, the wireless signals propagation characteristics may include additional information, including the expected variance of the signal strength measurements, the probability to receive signals at a specific point, etc.
Other types of radio maps are those where the wireless signals propagation characteristics comprise the propagation channel parameters at points or zones in the area, instead of the expected signal strength as previously described. Keeping the propagation channel parameters instead of expected signal strength values may provide the possibility to generate a more compact radio map since the same channel parameters may be used for large zones in an area and sometimes for a group of receivers or transceivers. In this case, the expected signal strength at a specific point in the area can be derived from the propagation channel parameters at that point.
Typically, the radio map generation technique works in two phases. In the so-called calibration phase (before the wireless system becomes operational), signal strengths (and if needed, also other parameters) are collected at multiple locations throughout the defined area (typically, but not necessarily an indoor area). The received signal strength measurements and optionally any additional information (e.g. message reception probability, variance of signal strength, etc.) may be stored together with the physical (e.g. x; y; z coordinates) locations at which they are measured. In an exemplary system, each (location; signal strength) entry is called a finger print and is stored in a database called a radio map. In another configuration, each entry of the radio map comprises of a single or plurality of points (e.g. a zone) with the corresponding channel parameters.
As previously mentioned, the radio maps may subsequently be used for various purposes according to the desired functionality of the wireless system. In a location system, the radio maps may be used by the location algorithms to estimate the location of wireless devices by correlating actual RSSI measurements to the data in the radio map.
Current methods of generating radio maps (either when used for wireless networks applications or for location), may require a calibration process (as explained before) of the area related to the radio map.
Typically the calibration process is a manual process and comprises collecting RSSI values and other parameters at a plurality of known zones or points in an area and this may be a lengthy process, particularly when large areas need to be calibrated.
Moreover, in addition to the long time that may be needed to initially calibrate an area, the relevance of the collected data tends to degrade over time. This is caused by changes in the environment (e.g. structural changes) or in the network (e.g. location of Access Points or other transceivers). Those changes may cause a degradation of the radio map relevance and consequently also a degradation of the location accuracy when this is applicable to location systems. In some cases, significant changes may require a new calibration process.
In addition, dynamic environmental changes like variation in the number of people present in an area, different humidity levels or different orientation of the mobile devices may result in a situation in which received signal strengths collected at one time may not accurately reflect the received signal strengths seen in the same locations at other times.
In other cases, the calibration process itself may require performing measurements in areas which have restricted access (e.g. patient rooms in hospitals, underground mines or hazardous areas).
It is desirable to provide improved methods, systems and devices for the generation of radio maps, regardless of the environment in which the radio map is to be used or the technical details of a network or system in which the radio map is to be employed (e.g., whether related to a pure wireless communication system or to a wireless communication and location system.)
Accordingly, in one embodiment of the disclosed embodiments, a method is provided to generate a radio map of an area, based on wireless signal measurements related to wireless mobile devices operating in that area, without necessarily using location information of the mobile units involved in the generation of the radio map in that area.
In another embodiment, a method is provided to generate a radio map of an area further comprising a continuous update of the radio map based on new measurements, thus providing improved performance in case of environmental and network changes.
The foregoing description is merely exemplary for providing general background and is not restrictive of the various embodiments of systems, methods, devices, and features as described and claimed.